2019 - The "Nights and Weekends Projects" Year in Review

It's almost the end of 2019, and I've been thinking back over the year as well as thinking ahead to 2020. In past years, I've often evaluated my "nights and weekends" projects using the same metrics I'm evaluated on at work: projects completed, and results obtained. This year, I've started looking my my "nights and weekends" efforts through a different lens focused more on the knowledge I've gained than just what I've produced.
As an aside, given the cover image, I do find it somewhat ironic that almost none of the knowledge I gained this year came from printed and bound books. Growing up with a love of libraries, and the fascinating collections of books they contained, it's both sad to think that knowledge is no longer concentrated there, and amazing to realize what a wealth of knowledge is now so easily-accessible just a short search away.

Looking back, there are two themes that run through several areas that I worked in across the year. The first of these is making software more modular, collaborative, and accessible. The second is Python. That's not all, though. So, let's get right to it!

Software Packaging and Distribution
Professionally, I come from a standard commercial-software background, and have often looked at open source through a similar lens. Specifically, I've often focused on software that can be packaged such that it's easily accessible to end users. This means bundling dependencies, providing installers, etc (see DVKit, a 'batteries-included' IDE for verification engineers).

This application-centric approach works well so long as the elements of functionality being distributed are relatively small in number, and the ways in which they need to be combined are fairly limited. This approach breaks down when the elements of functionality are relatively large in number, and need to be combined in many ways. In short, the more modular software becomes, the less feasible typical application-centric packaging becomes.

I've been dabbling for a few years in RTL design and verification. In this space, the verification environment for a given design will depend on many small elements of functionality -- utility libraries, reusable verification IP, etc. Bundling the dependencies with the verification environment quickly leads to projects that require lots of disk space. On the other hand, forcing users to download and install all the dependencies presents a significant barrier to new users.

One of the biggest reasons that I've spent so much time with Python this past year is that the Python ecosystem appears to provide a solution to this challenge of packaging and easily distributing small elements of functionality. Over the course of the year, I've spent time looking at Conda as a way of making application-level features more modular and easily-accessible. I've also spent time learning about how to package Python extension libraries (both with and without native library components) for distribution on PyPi, a repository for distributing Python packages.


New Approaches to Embedded DSLs
I've been involved in several projects over the years that have used C++ to provide a language-like user experience via C++ overloaded operators and macros. While there are certainly downsides to these embedded domain-specific languages in terms of error messaging and extensibility, an embedded domain-specific language can be a great way to prototype a language-based user interface before committing to the work of defining a first-class language and creating the parsing and processing infrastructure. It's also a very helpful approach for exploring new techniques in the context of existing languages.

C++ support for macros and operator overloading have been used for embedded DSLs from the beginning. However, using just these features tends to lead to somewhat awkward syntax, since operator overloading only supports expressions. C++11 (and beyond) brings new features, such as lambda expressions, and I spent time investigating these mechanisms and their impact on supporting expressing more-complex constructs in a more-natural way.

While the new C++11 features definitely showed promise, I started to wonder what support Python provided for implementing embedded domain-specific languages. As it turns out, Python provides some very powerful capabilities. Python supports overloading more operators than C++, and supports introspection into the code described by the user. I definitely intend to revisit embedded domain-specific languages captured in Python in 2020!

Constraint Solvers
Highly-capable constraint solvers that are available under permissive open-source licenses are becoming widely available, and I'm seeing these solvers applied to a range of interesting tasks. The CRAVE library for generating random stimulus has been around for some time. Several tools are leveraging available SMT solvers for model checking. Constraint solvers are even being applied for graphical layout of diagrams!

Given the range of applications to which solvers lend themselves, I thought it would be worth having a bit more hands-on knowledge. I spent some time learning about the Z3 solver API before concluding that, while the API is elegant and comprehensive, it's also more-complicated that what I need. I subsequently shifted to looking at the Boolector solver API, which is smaller and simpler.

The Boolector solver provides a Python binding, which is built along with the solver. This means that a user needs to manually build Boolector in order to use a Python package that uses the Boolector solver. Fortunately, I'd been learning about packaging and distributing Python extension libraries, and this this provided a perfect place to try this out. The Boolector Python library (PyBoolector) on PyPi is the result of this work.

Python for Verification
My background in verification is rooted in SystemC, SystemVerilog, and UVM. All very mainstream languages and methodologies in the commercial design and functional verification space. As I spent more time exploring Python and the modular and collaborative packaging it supports, I concluded that it made sense to investigate using Python for functional verification.

I spent time learning about cocotb, the most popular functional verification library in Python that I'm aware of. I also spent time learning about Python's back-end C API and how to structure bus-functional models to integrate at the procedure level with Python.

Actually, the more time I spend looking at Python for verification, the more possibilities I see. Definitely look for more on this topic in 2020!

In most areas, I've been quite happy with Python for verification. The object-oriented language features fit the requirements for high-level verification, and the easy availability of utility packages simplifies dealing with project dependencies. The one thing I've been dissatisfied with is support for static checking. I've used statically-typed languages for most application development. These languages have the advantage that the compiler can identify misuse of types before running the application. Dynamically-typed languages, such as Python and TCL, end up discovering type-misuse issues (eg passing an object to a method that expects an object of a different type) at runtime. One target for 2020 is learning more about what can be done to address this issue. Lint tools such as Pylint help, and my hope is to discover more tools and methodologies that help to close this gap.

RTL Design Skills
When I undertook the 2018 RISC-V Soft Core Contest, It had been quite a few years since I'd done any RTL design. Going through the design work for that project helped me brush up my skills quite a bit, but I knew I had quite a ways to go to be proficient. When the 2019 contest, centered around software security, came along, I knew it was a good opportunity to both learn more about software security vulnerabilities and improve my RTL design skills.

In addition to improving my RTL design skills, I learned a couple of things from initially attempting to add a few new features (multiplication, compressed instructions, security extensions) to my 2018 soft core. First, I had succeeded at writing some very good spaghetti RTL that wasn't modular enough to support extensibility. Furthermore, I didn't have sufficient tests to effectively and efficiently catch bugs introduced by adding new features.

Over the course of the 2019 project, I did a complete rewrite of the Featherweight RISC core. The more-modular structure of the rewritten core lends itself even better to bounded model checking, and I found this to be extremely helpful in catching and diagnosing bugs introduced during development and integration.

Going through this process also helped to improve my knowledge of RTL constructs that result in good efficient implementation, and which do not.


Looking Forward
2019 has been a great year for learning about more corners of the technical world. Looking forward to 2020, I see more work with Python, transitioning more of my existing projects over to cloud-based continuous integration, and more work with Python in the functional verification space. What will I learn along the way? Stay tuned for more blog posts across 2020 to find out!

As we come to the end of 2019 and the beginning of a new year (and new decade), I wish you happy holidays, a happy new year, and a 2020 ahead that is full of learning!

Disclaimer

The views and opinions expressed above are solely those of the author and do not represent those of my employer or any other party.

Py-HPI: Applying Python for Verification

Intro

In my last post, I talked about a prototype procedural interface between Python and HDL that enables cross-calling between Python and SystemVerilog. My primary motivation for investigating a procedural interface was its potential to maximize performance. In this post, I create a Python testbench for a small IP and compare it to the equivalent C++ testbench. I also look at the performance of Python for verification.

Creating a Python Testbench

My go-to IP for trying out new verification techniques is a small 32-bit RISC-V core named Featherweight RISC (FWRISC) that I created for a design contest last year. The original testbench was written in C++, so that will be my baseline for comparison. If you're interested in the structure of the testbench, have a look at this post.

Since I was keeping the testbench structure the same, I didn't expect much in terms of a reduction in lines of code. C++ is a bit verbose, in that it expects a header and implementation file for each class. This contributes to the fact that each C++ test is roughly twice as long as each Python test:

• C++ Test: 328 lines
• Python Test: 139 lines

Reducing the lines of code is a good thing, since more code statistically means more bugs, and spending time finding and fixing testbench bugs doesn't help us get our design verified. But, that's just the start.

The unit tests for FWRISC are all self-checking. This means that each assembly file contains the expected value for registers modified by the test. You can see the data embedded below between the start_expected and end_expected labels.

	entry:
	li x1, 5
	add x3, x1, 6
	j done
	// Expected value for registers
	start_expected:
	.word 1, 5
	.word 3, 11
	end_expected:

Because I didn't want to need to install an ELF-reading library on every machine where I wanted to run the FWRISC regression, I wrote my own small ELF-reading classes for the FWRISC testbench. This amounted to ~400 lines of code, and required a certain amount of thought and effort.

When I started writing the Python testbench, I thought about writing another ELF-reader in Python based on the code I'd written in C++... But then I realized that there was already a Python library for doing this called pyelftools. All I needed to do was get it installed in my environment (more on that in a future post), and call the API:

with open(sw_image, "rb") as f:
	elffile = ELFFile(f)
	symtab = elffile.get_section_by_name('.symtab')
	start_expected = symtab.get_symbol_by_name("start_expected")[0]["st_value"]
	end_expected = symtab.get_symbol_by_name("end_expected")[0]["st_value"]
	section = None
	for i in range(elffile.num_sections()):
	shdr = elffile._get_section_header(i)
	if (start_expected >= shdr['sh_addr']) and (end_expected <= (shdr['sh_addr'] + shdr['sh_size'])):
	start_expected -= shdr['sh_addr']
	end_expected -= shdr['sh_addr']
	section = elffile.get_section(i)
	break
	data = section.data()

That's a pretty significant savings both in terms of code, and in terms of development and debug effort! So, definitely my Python testbench is looking pretty good in terms of productivity. But, what about performance?

Evaluating Performance

Testbench performance may not be the most important factor when evaluating a language for use in verification. In general, the time an engineer takes to develop, debug, and maintain a verification environment is far more expensive than the compute time taken to execute tests. That said, understanding that performance characteristics of any language enables us to make smarter tradeoffs in how we use the language. 

I was fortunate enough to see David Patterson deliver his keynote A New Golden Age for Computer Architecture around a year ago at DAC 2018. The slide above comes from that presentation, and compares the performance of a variety of implementations of the computationally-intensive matrix multiply operation. As you can see from the slide, a C implementation is 50x faster than a Python implementation. Based on this slide and the anecdotal evidence of others, my pre-existing expectations were somewhat low when it came to Python performance. But, of course, having concrete data specific to functional verification is far more useful than a few anecdotes and rumors.

Spoiler alert: C++ is definitively faster than Python.

As with most languages, there are two aspects of performance to consider with Python: startup time and steady-state performance. Most of the FWRISC tests are quite short -- in fact, the suite of unit tests contains tests that execute less than 10 instructions.This gives us a good way to evaluate the startup overhead of Python. In order to evaluate the steady-state performance, I created a program that ran a tight loop with 10,000,000 instructions. The performance numbers below all come from Verilator-based simulations.

Startup Overhead
As I noted above, I evaluated the startup overhead of Python using the unit test suite. This suite contains 66 very short tests. 

• C++ Testbench: 7s
• Python Testbench: 18s

Based on the numbers above, Python does impose a noticeable overhead on the test suite -- it takes ~2.5x longer to run the suite with Python vs C++. That said, 18 seconds is still very reasonable to run a suite of smoke tests.

Steady-State Overhead
To evaluate the steady-state overhead of a Python testbench, I ran a long-loop test that ran a total of 10,000,000 instructions.

• C++ Testbench: 11.6s
• Python Testbench: 109.7s

Okay, this doesn't look so good. Our C++ testbench is 9.45x faster than our Python testbench. What do we do about this?

Adapting to Python's Performance

Initially, the FWRISC testbench didn't worry much about interaction between the design and testbench. The fwrisc_tracer BFM called the testbench on each executed instruction, register write, and memory access. This was, of course, simple. But, was it really necessary?

Actually, in most cases, the testbench only needs to be aware of the results of a simulation, or key events across the simulation. Given the cost of calling Python, I made a few optimizations to the frequency of events sent to the testbench:

• Maintain the register state in the tracer BFM, instead of calling the testbench every time a write occurs. The testbench can read back the register state at the end of the test as needed.
• Notify the testbench when a long-jump or jump-link instruction occurs, instead of on every instruction. This allows the testbench to detect end-of-test conditions and minimizes the frequency of calls

With these two enhancements to both the C++ and Python testbenches, I re-ran the long-loop test and got new results:

• C++ Testbench: 4s
• Python Testbench: 5s

Notice that the C++ results have improved as well. My interpretation of these results is that most of the time is now spent by Verilator in simulating the design, and the results are more-or-less identical.

Conclusions

The Python ecosystem brings definite benefits when applying Python for functional verification. The existing ecosystem of available libraries, and the infrastructure to easily access them, simplifies the effort needed to reuse existing code. It also minimizes the burden placed on users that want to try out an open source project that uses Python for verification.

Using Python does come with performance overhead. This means that it's more important to consider how the execution of the testbench relates to execution of the design. A testbench that interacts with the design frequently (eg every clock) will impose much greater overhead compared to a testbench that interacts with the design every 100 or 1000 cycles. There are typically many optimization opportunities that minimize the performance overhead of a Python testbench, while not adversely impacting verification results.

It's important to remember that engineer time is much more expensive than compute time, so making engineers more productive wins every time. So, from my perspective, the real question isn't whether C++ is faster than Python. The real questions are whether Python is sufficiently fast to be useful, and whether there are reasonable approaches to dealing with the performance bottlenecks. Based on my experience, the answer is a resounding Yes. 

Disclaimer

The views and opinions expressed above are solely those of the author and do not represent those of my employer or any other party.